Problems of the form in (REF ) are generalizations of the standard constrained stochastic optimization problem which is obtained when \(T=1\) , and arise in several machine learning applications. Some examples include sparse additive modeling in non-parametric statistics [1]}, Bayesian optimization [2]}, model-agnostic meta-learning [3]}, distributionally robust optimization [4]}, training graph neural networks [5]} and reinforcement learning [6]}. Below, we provide a concrete motivating example from the field of risk-averse stochastic optimization [7]}.
